• Energy Research
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Abstract. Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive or negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative El Niño–Southern Oscillation (ENSO) phases when the sea surface temperature (SST) anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing, however, does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.

Electrolyte imbalance caused by the undesired vanadium-ions cross-over and water transport through the membrane is one of the main critical issues of vanadium redox flow batteries, leading to battery capacity loss and electrolytes volume variation. In this work, the evolution of discharged capacity and electrolyte volume variation were firstly investigated adopting commercial electrolyte for hundreds of charge-discharge cycles in vanadium redox flow batteries employing different membranes, varying thickness and equivalent weight. Subsequently, with the support of a 1D physics-based model, the origin of the main phenomena regulating capacity decay and volume variation has been identified and different modifications in the preparation of electrolytes have been proposed. Electrolytes characterized by an equal proton concentration between the two tanks at the beginning of cycling operation turned out to limit capacity decay, while increasing electrolyte proton concentration was effective also in the mitigation of volume variation. The most promising electrolyte preparation combined the effect of high proton concentration and null osmotic pressure gradient between the two tanks: compared to commercial electrolyte this preparation reduced the capacity decay from 47.7% to 20.9%, increased the coulombic efficiency from 96.2% to 98.9% and the energy one from 79.9% to 83.4%, and also implied a negligible volume variation during cycles. The effectiveness of this electrolyte preparation has been verified with different membranes, increasing the range of validity of the results, that could be thus applied in a real system regardless of the adopted membrane.

Abstract The use of simplifying techniques to obtain skeletal kinetic mechanisms with the required accuracy is often a necessary step when computationally demanding simulations are concerned. In this work, a novel approach for an automatic mechanism reduction, aimed at retaining accuracy on specific target species, is proposed. Starting from the consolidated coupling between flux analysis and sensitivity analysis, a methodology based on curve matching and functional data analysis was developed, through which the importance of a species in the target accuracy is assessed via a proper metric. The error associated with the removal of uncertain species from the detailed mechanism is quantified in terms of distance and similarity indices before and after such a removal, within a Species-Targeted Sensitivity Analysis (STSA) framework. A species ranking is then generated, and the original mechanism is progressively reduced. The whole algorithm also implements several improvements to enhance a faster convergence, and adds a novel criterion to remove unimportant reactions, based on sensitivity analysis to kinetic parameters. The capability of this algorithm was tested through two case studies in this work. A kinetic mechanism for a Toluene Reference Fuel (TRF) was first obtained, with the overall reactivity as reduction target. The numerical procedure allowed to obtain a compact skeletal mechanism (115 species and 856 reactions), able to retain good accuracy in ignition delay time and laminar flame speed predictions of both fuel mixture and pure compounds. More important, two skeletal mechanisms for methane combustion, including chemistry of nitrogen oxides (NO x ), were developed, with different degrees of reduction. The agreement between the original and the skeletal mechanisms in terms of NO formation was successfully assessed with satisfactory results. Attention was also dedicated to the choice of the type of reactor where undertaking reduction, which turned out to play a major role in the overall process.

This paper deals with wave energy conversion in shallow water, analyzing the performance of two different oscillating-body systems. The first one is a heaving float, which is a system known in the literature. The second one is obtained by coupling the heaving float with a surging paddle. In order to check the different behaviors of the multibody system and the single-body heaving float, physical models of the two systems have been tested in a wave flume, by placing them at various water depths along a sloping bottom. The systems have been tested with monochromatic waves. For each water depth, several tests have been performed varying the geometrical and mechanical parameters of the two systems, in order to find their best configurations. It has been found that the multibody system is more energetic when the float and the paddle are close to each other. Capture width ratio has been found to significantly vary with water depth for both systems: in particular, capture width ratio of the heaving float (also within the multibody system) increases as water depth increases, while capture width ratio of the paddle (within the multibody system) increases as water depth decreases. At the end, the capture width ratio of the multibody system is almost always higher than that of the heaving float, and it increases as water depth increases on average; however, the multibody advantage over single body is significant for water depth less than the characteristic dimension of the system, and decreases as water depth increases.

Abstract With its scientifically proven effects and widespread acceptance, climate change stands as one of the most pressing and intricate challenges for society and economies. Farmers are on the frontline of managing climate change. Therefore, how they perceive and respond to climate change shapes their risk assessment and structures future resilience and adaptive capacity. Employing a bottom-up approach, we conducted 460 surveys randomly among farmers throughout the Lombardy region in northern Italy. A triple-loop approach considering climate change awareness, perceived impacts, and adaptation measures and barriers was implemented to characterize the potential heterogeneity of farmer behaviour and explore whether risk attitudes and adaptation actions differ between farmer profiles. We then profiled farmers through a clustering analysis. Four groups emerged, highlighting farmers’ adaptation preferences and risk attitudes: (1) cropping-adapted and isolated farmers, (2) cooperation-adapted and insecure farmers, (3) risk insurance-adapted and confident farmers, and (4) climate services-adapted and aware farmers. Although the groups vary in terms of farmers’ characterization and farming activities (e.g. young and highly educated vs. old and highly experienced farmers, rainfed vs. irrigated farms), similar patterns were observed regarding climate change awareness and perceived impacts—however, the contrast increased in terms of adaptation measures and barriers. Gaining a more comprehensive understanding of the diverse ways in which farmers assess risks and adapt can promote the transferability of bottom-up findings and inform the co-design of tailored and flexible adaptation instruments, minimizing the risk of maladaptation or ineffective transformation in the face of climate change.

AbstractThis article describes a control approach for large‐scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118‐bus system is used to test the proposed design procedure in a nontrivial scenario.

The Sustainable Development Goals (SDGs) of the United Nations state that cities and human settlements need to be more inclusive, safe and resilient. In Europe cities have experienced dramatic physical, social and economic changes during the last decades while historic centres of European cities, among the most important assets of the European cultural heritage, are living paradoxes. They are defined as “a collection of beauty, icon of well-being, model of sustainability, but abandoned”. This study investigates the changes in the urban landscape of Nicosia, a particular historical centre in the Mediterranean region (Cyprus). The city centre is characterised by exceptionally well-preserved Venetian fortifications. Due to political circumstances, the capital of Cyprus, Nicosia, is still divided and has been ruled by two different administrations for several decades. This study used optical multi-spectral satellite datasets processing, like the Landsat and the most recent Sentinel-2 products, to detect, identify and characterise significant morphological transformations within the walled city and around it. This paper’s central thesis promotes a more systematic use of earth observation products and derivatives in decision-making processes that regard planning, use and management of urban resources in Europe, especially in support of urban planning strategies of historic cities.